Quantitative Comparison of LiDAR Point Cloud Segmentation for Autonomous Vehicles

Anand, Bhaskar and Barsaiyan, Vivek and Senapati, Mrinal and Rajalakshmi, P (2021) Quantitative Comparison of LiDAR Point Cloud Segmentation for Autonomous Vehicles. In: 94th IEEE Vehicular Technology Conference, VTC 2021-Fall, 27 September 2021 through 30 September 2021, Virtual, Online.

[img] Text
Technology_Conference.pdf - Published Version
Restricted to Registered users only

Download (399kB) | Request a copy


The Light detection and ranging (LiDAR) sensor is used for perceiving the environment of an autonomous vehicle. LiDAR data or point cloud is processed to get the obstacles and their speed around an autonomous vehicle. Based on the information retrieved from LiDAR data and the data from other sensors, real-time decisions are taken for the proper navigation. Hence, the time taken in LiDAR data processing should be minimized. One of the important steps for LiDAR data processing is the segmentation of the obstacles. In this paper, we present a quantitative comparison between two different approaches for point cloud segmentation, Euclidean distance-based Cluster Extraction and Cylindrical range image-based method. Based on the simulation performed on ROS (Robot Operating System) platform, we found that the second method is much faster as compared to the first method. In addition to that, the second method can perform proper segmentation at a larger distance from the sensor. © 2021 IEEE.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Rajalakshmi, Phttps://orcid.org/0000-0002-7252-6728
Item Type: Conference or Workshop Item (Paper)
Additional Information: This work is supported by the project ”Real-Time Edge Computing Architectures for LiDAR based Intelligent Transportation Systems”, funded by Ministry of Electronics and Information Technology (MeitY), Government of India.
Uncontrolled Keywords: Autonomous vehicle; LiDAR; Point cloud; Segmentation
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 21 Sep 2022 12:54
Last Modified: 21 Sep 2022 12:54
URI: http://raiith.iith.ac.in/id/eprint/10643
Publisher URL: http://doi.org/10.1109/VTC2021-Fall52928.2021.9625...
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 10643 Statistics for this ePrint Item